One popular way to run AI locally has been to use multiple GPUs to access more memory and compute. While cloud frameworks like vLLM are well optimized for multiple GPUs thanks to their use in data centers, PC frameworks like llama.cpp and the ComfyUI implementation in PyTorch are not optimized for it. To solve this challenge, NVIDIA has collaborated with both llama.cpp and ComfyUI to enhance performance for RTX PCs with two equivalent GPUs. This enables you to run larger models and use the compute of both GPUs for better performance. llama.cpp now supports tensor parallelism (TP), fully utiliz
Build Personal AI Agents on Windows PCs with New Tools from Microsoft and NVIDIA | NVIDIA Technical Blog… WDDM Trace new option: WDDM Memory Trace This option captures a focused set of WDDM memory events to track VRAM management. This generates reduced overhead and smaller report files allowing longer trace sessions focused on Windows Graphics VRAM management. …
… With secure agent sandboxing, faster inference, multi-GPU scaling, and a maturing Windows AI platform, developers building on the over 100 million NVIDIA RTX PCs worldwide have the infrastructure to ship the next generation of AI applications. Learn more and start developing for NVIDIA RTX AI PCs . …
… Splitting optimization into a hardware-agnostic "ahead-of-time" AOT phase and a hardware-specific "just-in-time" JIT phase in order to improve user experience. …
… This standalone Windows service manages CloudXR runtime instances through an RPC interface. …
… He works on GPU compute infrastructure with a focus on GPU scheduling to enable AI and HPC workloads to scale on Kubernetes. …
… This enables self-evolving claws to run more safely in the cloud, on prem, on NVIDIA RTX PCs, and on NVIDIA DGX Spark. …
… BESS is an integrated system that combines battery cells with power conversion systems PCS inverters, advanced telemetry, and dynamic control schemes. …